predict_cv2: Get train/test splits of the phenotypic MET dataset based on...

In cjubin/learnMET: Predictions with Machine Learning methods in Multi Environment Trials (MET)

Get train/test splits of the phenotypic MET dataset based on CV2.

Description

Get train/test splits of the phenotypic MET dataset based on a number of random k-folds partitions determined by the user, according to the type CV2. Creation of the list of train/test splits based on phenotypic data, so that all the Year x Location phenotypic observations from the phenotypic MET dataset are assigned randomly to k-fold partitions (prediction of incomplete field trials).

Usage

predict_cv2(pheno_data, nb_folds, reps, seed)

Arguments

pheno_data	data.frame Dataset containing phenotypic outcome data, as well as the predictor variables
nb_folds	numeric Number of folds in the CV process
reps	numeric Number of repeats of the k-folds CV

Value

a cv_object object which contains nb_folds x reps elements. Each element of the object corresponds to a split object with two elements:

training

data.frame Dataset with all observations for the training set.

test

data.frame Dataset with all observations for the test set.

Author(s)

Cathy C. Westhues [e-mail]

References

\insertRef

jarquin2017increasinglearnMET \insertRefjarquin2014reactionlearnMET

cjubin/learnMET documentation built on Nov. 4, 2024, 6:23 p.m.